Predicting Survival in Patients Undergoing Resection for Locally Recurrent Retroperitoneal Sarcoma: A Study and Novel Nomogram from TARPSWG
Bibliographic record
Abstract
PURPOSE: The role of surgery for first relapse locally recurrent retroperitoneal sarcoma (RPS-LR1) is uncertain. We report outcomes of the largest RPS-LR1 series and propose a new prognostic nomogram. EXPERIMENTAL DESIGN: Patients with consecutive RPS-LR1 without distant metastases who underwent resection at 22 centers (2002-2011) were included. Endpoints were disease-free and overall survival (DFS, OS) and crude-cumulative-incidence (CCI) of local/distant recurrence from second surgery. Nomograms predicting DFS and OS from second surgery were developed and validated (calibration plots); discrimination was assessed (Harrell C index). RESULTS: Of 684 patients identified, full prognostic variable data were available for 602. Initial surgery for primary RPS was performed at our institutions in 188 patients (31%) and elsewhere in 414 (69%). At a median follow-up of 119 months [Interquartile range (IQR), 80-169] from initial surgery and 75 months (IQR 50-105) from second surgery, 6-year DFS and OS were 19.2% [95% confidence interval (CI), 16.0-23.0%] and 54.1% (95% CI, 49.8-58.8%), respectively. Recurrence patterns and survival probability were histology-specific, with liposarcoma subtypes having the highest 6-year CCI of second local recurrence (LR, 60.2%-70.9%) and leiomyosarcoma (LMS) having higher 6-year CCI of distant metastasis (DM, 36.3%). Nomograms included age at second surgery, multifocality, grade, completeness of second surgery, histology, chemotherapy/radiotherapy at first surgery, and number of organs resected at first surgery. OS and DFS nomograms showed good calibration and discriminative ability (C index 0.70 and 0.67, respectively). CONCLUSIONS: We developed nomograms to predict DFS and OS for patients undergoing RPS-LR1 resection. Nomograms provide individualized, disease-relevant estimations of survival for RPS-LR1 patients and assist in clinical decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".